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Register a new userEstimating causes of maternal death in data-sparse contexts
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Understanding the underlying causes of maternal death across all regions of the world is essential to inform policies and resource allocation to reduce the mortality burden. However, in many countries of the world there exists very little data on the causes of maternal death, and data that do exist do not capture the entire population of risk. In this paper we present a Bayesian hierarchical multinomial model to estimate maternal cause of death distributions globally, regionally and for all countries worldwide. The framework combines data from various sources to inform estimates, including data from civil registration and vital systems, smaller-scale surveys and studies, and high-quality data from confidential enquiries and surveillance systems. The framework accounts of varying data quality and coverage, and allows for situations where one or more causes of death are missing. We illustrate the results of the model on three case study countries that have different data availability situations: Canada, Nigeria and the United States.
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In this paper, we clarify the mechanisms underlying a general phenomenon present in pulse-coupled heterogeneous inhibitory networks: inhibition can induce not only suppression of the neural activity, as expected, but it can also promote neural reactivation. In particular, for globally coupled systems, the number of firing neurons monotonically reduces upon increasing the strength of inhibition (neurons death). However, the random pruning of the connections is able to reverse the action of inhibition, i.e. in a sparse network a sufficiently strong synaptic strength can surprisingly promote, rather than depress, the activity of the neurons (neurons rebirth). Thus the number of firing neurons reveals a minimum at some intermediate synaptic strength. We show that this minimum signals a transition from a regime dominated by the neurons with higher firing activity to a phase where all neurons are effectively sub-threshold and their irregular firing is driven by current fluctuations. We explain the origin of the transition by deriving an analytic mean field formulation of the problem able to provide the fraction of active neurons as well as the first two moments of their firing statistics. The introduction of a synaptic time scale does not modify the main aspects of the reported phenomenon. However, for sufficiently slow synapses the transition becomes dramatic, the system passes from a perfectly regular evolution to an irregular bursting dynamics. In this latter regime the model provides predictions consistent with experimental findings for a specific class of neurons, namely the medium spiny neurons in the striatum.
Though they may offer valuable patient and disease information that is impossible to study in a randomized trial, clinical disease registries also require special care and attention in causal inference. Registry data may be incomplete, inconsistent, and subject to confounding. In this paper we aim to address several analytical issues in estimating treatment effects that plague clinical registries such as the Emory amyotrophic lateral sclerosis (ALS) Clinic Registry. When attempting to assess the effect of a surgical insertion of a percutaneous endoscopic gastrostomy (PEG) tube on body mass index (BMI) using the data from the ALS Clinic Registry, one must combat issues of confounding, censoring by death, and missing outcome data that have not been addressed in previous studies of PEG. We propose a causal inference framework for estimating the survivor average causal effect (SACE) of PEG, which incorporates a model for generalized propensity scores to correct for confounding by pre-treatment variables, a model for principal stratification to account for censoring by death, and a model for the missing data mechanism. Applying the proposed framework to the ALS Clinic Registry Data, our analysis shows that PEG has a positive SACE on BMI at month 18 post-baseline; our results likely offer more definitive answers regarding the effect of PEG than previous studies of PEG.
Cell fate decisions in multicellular organisms are precisely coordinated, leading to highly reproducible macroscopic outcomes of developmental processes. The origins of this reproducibility can be found at the molecular level during the earliest stages of development when spatial patterns of morphogen (form-generating) molecules emerge reproducibly. However, the initial conditions for these early stages are determined by the female during oogenesis, and it is unknown whether reproducibility is passed on to the zygote or whether it is reacquired by the zygote. Here we examine the earliest reproducible pattern in the Drosophila embryo, the Bicoid protein gradient. Using a unique combination of absolute molecule counting techniques, we show that it is generated from a highly controlled source of mRNA molecules that is reproducible from embryo to embryo to within ~8%. This occurs in a perfectly linear feed-forward process: changes in the females gene dosage lead to proportional changes in the mRNA and protein counts in the embryo. In this setup, noise is kept low in the transition from one molecular species to another, allowing the female to precisely deposit the same absolute number of mRNA molecules in each embryo and therefore confer reproducibility to the Bicoid pattern. Our results indicate that the reproducibility of the morphological structures that emerge in the embryo originates during oogenesis when all initial patterning signals are controlled with precision similar to what we observe for the Bicoid pattern.
We explore what causes business cycles by analyzing the Japanese industrial production data. The methods are spectral analysis and factor analysis. Using the random matrix theory, we show that two largest eigenvalues are significant. Taking advantage of the information revealed by disaggregated data, we identify the first dominant factor as the aggregate demand, and the second factor as inventory adjustment. They cannot be reasonably interpreted as technological shocks. We also demonstrate that in terms of two dominant factors, shipments lead production by four months. Furthermore, out-of-sample test demonstrates that the model holds up even under the 2008-09 recession. Because a fall of output during 2008-09 was caused by an exogenous drop in exports, it provides another justification for identifying the first dominant factor as the aggregate demand. All the findings suggest that the major cause of business cycles is real demand shocks.
Empirical analysis results about the possible causes leading to non-citation may help increase the potential of researchers work to be cited and editorial staffs of journals to identify contributions with potential high quality. In this study, we conduct a survey on the possible causes leading to citation or non-citation based on a questionnaire. We then perform a statistical analysis to identify the major causes leading to non-citation in combination with the analysis on the data collected through the survey. Most respondents to our questionnaire identified eight major causes that facilitate easy citation of ones papers, such as research hotspots and novel topics of content, longer intervals after publication, research topics similar to my work, high quality of content, reasonable self-citation, highlighted title, prestigious authors, academic tastes and interests similar to mine.They also pointed out that the vast difference between their current and former research directions as the primary reason for their previously uncited papers. They feel that text that includes notes, comments, and letters to editors are rarely cited, and the same is true for too short or too lengthy papers. In comparison, it is easier for reviews, articles, or papers of intermediate length to be cited.
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